regenerate tutorial docs for v0.4.0

docs/index.rst

@@ -82,11 +82,11 @@ Up to versions 0.1.x this package was called *TimeEvolvingMPO*.
    :maxdepth: 1
    :caption: Tutorials
 
-   pages/tutorials/quickstart/quickstart
-   pages/tutorials/pt_tempo/pt_tempo
-   pages/tutorials/bath_dynamics/bath_dynamics
-   pages/tutorials/pt_tebd/pt_tebd
-   pages/tutorials/mf_tempo/mf_tempo
+   pages/tutorials/quickstart
+   pages/tutorials/pt_tempo
+   pages/tutorials/bath_dynamics
+   pages/tutorials/pt_tebd
+   pages/tutorials/mf_tempo
 
 .. toctree::
    :maxdepth: 1

docs/pages/tutorials/bath_dynamics/bath_dynamics.rst → docs/pages/tutorials/bath_dynamics.rst

@@ -157,7 +157,7 @@ matrix elements evolve.
 
 
 
-.. image:: output_10_2.png
+.. image:: bath_dynamics_files/bath_dynamics_10_2.png
 
 
 Already the process tensor tells us everything we could want to know
@@ -254,7 +254,7 @@ frequency, in this case let’s look at ``w = Omega`` and a bandwidth of
 
 
 
-.. image:: output_14_2.png
+.. image:: bath_dynamics_files/bath_dynamics_14_2.png
 
 
 …that took quite a while. From the expression for :math:`\Delta Q` is
@@ -287,14 +287,13 @@ see what happens if we want the energy of another mode now, let’s say
 
 
 
-.. image:: output_16_1.png
+.. image:: bath_dynamics_files/bath_dynamics_16_1.png
 
 
 Much quicker! This is because the same set of system correlation
 functions can be used to compute any bath correlation function
 :math:`\langle \alpha_2(t_2)\alpha_1(t_1)\rangle` where
-$:raw-latex:`\alpha`\_2, :raw-latex:`\alpha`\_1
-:raw-latex:`\in `{a_k^:raw-latex:`\dagger`,a_k} $ and
+:math:`\alpha_2, \alpha_1 \in \{a_k^\dagger,a_k\}`  and
 :math:`t_1,t_2 < t_N`. So now we see the logic of having a bath_dynamics
 object, it allows us to conveniently store the calculated system
 correlation functions and re-use them as we like :)
@@ -331,7 +330,7 @@ exchanged over the process so simply look at the final value of
 
 
 
-.. image:: output_18_1.png
+.. image:: bath_dynamics_files/bath_dynamics_18_1.png
 
 
 This is highly oscillatory, perhaps unsurprising from the dynamics we
@@ -366,7 +365,7 @@ we average over the last :math:`n` timesteps where
 
 
 
-.. image:: output_20_1.png
+.. image:: bath_dynamics_files/bath_dynamics_20_1.png
 
 
 Here, as in the paper, we see heat is absorbed by the system from the
@@ -375,4 +374,3 @@ band of the modes in the vicinity of
 as in a Markovian theory the system would sample the environment purely
 at its eigensplitting :math:`\tilde{\Omega}`.
 
---------------

docs/pages/tutorials/mf_tempo/mf_tempo.rst → docs/pages/tutorials/mf_tempo.rst

@@ -36,7 +36,7 @@ We firstly import OQuPy and other useful packages:
     import matplotlib.pyplot as plt
 
 Check the current OQuPy version; mean-field functionality was introduced
-in version **0.3.0**.
+in version **0.3.0** and revised in its current format in **0.4.0.**
 
 .. code:: ipython3
 
@@ -352,8 +352,8 @@ used to compute the dynamics for an ordinary ``System``:
 .. parsed-literal::
 
     --> TEMPO-with-field computation:
-    100.0%   93 of   93 [########################################] 00:00:19
-    Elapsed time: 20.0s
+    100.0%   93 of   93 [########################################] 00:00:13
+    Elapsed time: 13.3s
 
 
 ``MeanFieldTempo.compute`` returns a ``MeanFieldDynamics`` object
@@ -385,12 +385,12 @@ producing the first part of a single line of **Fig. 2a.**:
 
 .. parsed-literal::
 
-    <matplotlib.legend.Legend at 0x7f6c669133c8>
+    <matplotlib.legend.Legend at 0x7f848ff957e0>
 
 
 
 
-.. image:: output_34_1.png
+.. image:: mf_tempo_files/mf_tempo_34_1.png
 
 
 If you have the time you can calculate the dynamics to
@@ -428,8 +428,8 @@ bath via
 .. parsed-literal::
 
     --> PT-TEMPO computation:
-    100.0%   93 of   93 [########################################] 00:00:06
-    Elapsed time: 6.6s
+    100.0%   93 of   93 [########################################] 00:00:02
+    Elapsed time: 2.8s
 
 
 Refer the Time Dependence and PT-TEMPO tutorial for further discussion
@@ -482,17 +482,17 @@ using the process tensor (now in a list) calculated above:
 .. parsed-literal::
 
     --> Compute dynamics with field:
-    100.0%   93 of   93 [########################################] 00:00:15
-    Elapsed time: 15.0s
+    100.0%   93 of   93 [########################################] 00:00:09
+    Elapsed time: 9.6s
     --> Compute dynamics with field:
-    100.0%   93 of   93 [########################################] 00:00:14
-    Elapsed time: 14.0s
+    100.0%   93 of   93 [########################################] 00:00:09
+    Elapsed time: 9.9s
     --> Compute dynamics with field:
-    100.0%   93 of   93 [########################################] 00:00:15
-    Elapsed time: 15.8s
+    100.0%   93 of   93 [########################################] 00:00:09
+    Elapsed time: 9.9s
     --> Compute dynamics with field:
-    100.0%   93 of   93 [########################################] 00:00:14
-    Elapsed time: 14.6s
+    100.0%   93 of   93 [########################################] 00:00:08
+    Elapsed time: 8.7s
 
 
 Finally, plotting the results:
@@ -513,12 +513,12 @@ Finally, plotting the results:
 
 .. parsed-literal::
 
-    <matplotlib.legend.Legend at 0x7f6c66697b38>
+    <matplotlib.legend.Legend at 0x7f848f7e88b0>
 
 
 
 
-.. image:: output_43_1.png
+.. image:: mf_tempo_files/mf_tempo_43_1.png
 
 
 5. Summary
@@ -535,4 +535,3 @@ dynamics:
    (process tensor) may be used to calculate ``MeanFieldDynamics``
 -  ``MeanFieldDynamics`` comprises one of more system ``Dynamics`` and a
    set of field values ``fields``.
-

docs/pages/tutorials/pt_tebd/pt_tebd.rst → docs/pages/tutorials/pt_tebd.rst

@@ -69,7 +69,6 @@ density matrices.
     down_dm = oqupy.operators.spin_dm("z-")
     mixed_dm = oqupy.operators.spin_dm("mixed")
 
-
 --------------
 
 Example - Heisenberg spin chain
@@ -91,8 +90,6 @@ coupling we first consider the closed chain with the Hamiltonian
 with :math:`N=5`, :math:`\epsilon=1.0`, :math:`J^x = 1.3`,
 :math:`J^y = 0.7`, and :math:`J^z = 1.2`.
 
-
-
 .. code:: ipython3
 
     N = 5
@@ -179,7 +176,7 @@ time steps.
     PT-TEBD computation (closed spin chain):
     --> PT-TEBD computation:
     100.0%   20 of   20 [########################################] 00:00:01
-    Elapsed time: 1.1s
+    Elapsed time: 1.2s
 
 
 The computation returns a results dictionary which in addition to the
@@ -208,20 +205,20 @@ We can use the dynamics results to then compute the evolution of the
         plt.plot(
             *dynamics.expectations(sz, real=True),
             color=f"C{site}", linestyle="solid",
-            label=f"$<s_{site}^z>$")
+            label=f"$\\langle s_{site}^z \\rangle$")
     plt.legend()
 
 
 
 
 .. parsed-literal::
 
-    <matplotlib.legend.Legend at 0x7f672e2d7588>
+    <matplotlib.legend.Legend at 0x7f7b31700f40>
 
 
 
 
-.. image:: output_23_1.png
+.. image:: pt_tebd_files/pt_tebd_22_1.png
 
 
 Yay! We can observe how the excitation travels along the chain.
@@ -288,7 +285,7 @@ computation (see Tutorial 02 - Time dependence and PT-TEMPO).
     Process tensor (PT) computation:
     --> PT-TEMPO computation:
     100.0%   20 of   20 [########################################] 00:00:00
-    Elapsed time: 0.6s
+    Elapsed time: 0.4s
 
 
 To see the effect of the environment clearly we start in a fully mixed
@@ -326,29 +323,29 @@ keep them free by setting them to ``None``.
 
     PT-TEBD computation (open spin chain):
     --> PT-TEBD computation:
-    100.0%   20 of   20 [########################################] 00:00:04
-    Elapsed time: 4.3s
+    100.0%   20 of   20 [########################################] 00:00:02
+    Elapsed time: 2.9s
 
 
 .. code:: ipython3
 
     for site, dyn in results_open['dynamics'].items():
         plt.plot(*dyn.expectations(sz, real=True),
                  color=f"C{site}", linestyle="solid",
-                 label=f"$<s_{site}^z>$")
+                 label=f"$\\langle s_{site}^z \\rangle$")
     plt.legend()
 
 
 
 
 .. parsed-literal::
 
-    <matplotlib.legend.Legend at 0x7f671cf195f8>
+    <matplotlib.legend.Legend at 0x7f7b30febca0>
 
 
 
 
-.. image:: output_35_1.png
+.. image:: pt_tebd_files/pt_tebd_34_1.png
 
 
 We can see that the environment starts to hybridize with the first spin

docs/pages/tutorials/pt_tempo/pt_tempo.rst → docs/pages/tutorials/pt_tempo.rst

@@ -168,12 +168,12 @@ detuning, we can check the shape of the laser pulse.
 
 .. parsed-literal::
 
-    <matplotlib.legend.Legend at 0x7f42993e3278>
+    <matplotlib.legend.Legend at 0x7fab3d70bb20>
 
 
 
 
-.. image:: output_16_1.png
+.. image:: pt_tempo_files/pt_tempo_16_1.png
 
 
 3. Create time dependent system object
@@ -216,7 +216,7 @@ parameters):
 
     --> TEMPO computation:
     100.0%   50 of   50 [########################################] 00:00:02
-    Elapsed time: 2.3s
+    Elapsed time: 2.6s
 
 
 and extract the expectation values
@@ -230,7 +230,7 @@ for plotting:
     s_xy = np.sqrt(s_x**2 + s_y**2)
     plt.plot(t, s_xy, label=r'$\Delta = 0.0$')
     plt.xlabel(r'$t\,\Omega$')
-    plt.ylabel(r'$<\sigma_xy>$')
+    plt.ylabel(r'$<\sigma_{xy}>$')
     plt.ylim((0.0,1.0))
     plt.legend(loc=4)
 
@@ -239,12 +239,12 @@ for plotting:
 
 .. parsed-literal::
 
-    <matplotlib.legend.Legend at 0x7f42980cb898>
+    <matplotlib.legend.Legend at 0x7fab3ab8f430>
 
 
 
 
-.. image:: output_23_1.png
+.. image:: pt_tempo_files/pt_tempo_23_1.png
 
 
 5. Using PT-TEMPO to explore many different laser pulses
@@ -331,7 +331,7 @@ and plot :math:`\langle\sigma_{xy}\rangle(t)` for each:
     for t, s_xy, delta in zip(t_list, s_xy_list, deltas):
         plt.plot(t, s_xy, label=r"$\Delta = $"+f"{delta:0.1f}")
         plt.xlabel(r'$t/$ps')
-        plt.ylabel(r'$<\sigma_xy>$')
+        plt.ylabel(r'$<\sigma_{xy}>$')
     plt.ylim((0.0,1.0))
     plt.legend()
 
@@ -340,11 +340,11 @@ and plot :math:`\langle\sigma_{xy}\rangle(t)` for each:
 
 .. parsed-literal::
 
-    <matplotlib.legend.Legend at 0x7f42991f66d8>
+    <matplotlib.legend.Legend at 0x7fab3abec4c0>
 
 
 
 
-.. image:: output_31_1.png
+.. image:: pt_tempo_files/pt_tempo_31_1.png